Since early in the history of the use of electroencephalogram (EEG) for measurement of electrical patterns of the human brain, efforts have been made to transform EEG electrical activity into sound and music. These efforts not only created alternatives to purely visual feedback, but also opened up new possibilities for artistic and musical expression, and created possible therapeutic avenues for patients with various causes of motor disability.
The earliest example of such EEG to sound conversion appears to occur in the literature shortly after the invention of the EEG. In 1934, Adrian and Matthews, replicating the earliest EEG descriptions of the posterior dominant rhythm (‘the Berger rhythm’) by Hans Berger in 1929, monitored their own EEG with sound.
In 1965, the composer and experimental musician Alvin Lucier created a performance involving control of percussion instruments via strength of EEG posterior dominant rhythm (PDR) at the Rose Art Museum at Brandeis University, with the encouragement and participation of composer John Cage. The performer increased and decreased the volume of percussion instruments by modulation of the PDR with opening and closing their eyes. However, they experienced some difficulty in achieving good control, and to overcome this employed a second performer manually adjusting the gain from the EEG output.
Following in Lucier's pathway 5 years later, David Rosenboom in 1970 created a performance piece called “Ecology of the Skin” for Automation House in New York, N.Y. This involved using EEG signal from ten participants processed through individualized electronic circuits to generate visual and auditory performance. More recently, in 2006, Brouse et al., created EEG waveform spectral analysis in multiple frequency bands to passively control sound and music, in a project for the eNTERFACE summer workshop.
Eduardo Miranda at the Interdisciplinary Centre for Computer Music Research (ICCMR) at Plymouth University, UK was part of that summer workshop project, and has gone on to contribute significantly to this field. In 2008, he used the changing patterns of alpha and beta frequency rhythms in EEG to act as a switch between different musical styles, and later used subject visual gaze direction to allow visual evoked potentials of EEG to control various musical parameters. More recently, Miranda and colleagues used a statistical analysis of subjective emotions and EEG in an attempt to create an emotion sensor to subconsciously allow users to select music which they associate with more subjectively positive emotions.
Mirko Pham and others used slow cortical potentials of EEG to drive control of either ascending or descending pre-set pitch sequences; they used both auditory feedback and visual feedback. While they used tone sequences for feedback, the feedback did not represent a musical context. Using this protocol, they observed significantly better results for visual than auditory feedback.
Brain Computer Interface (BCI) research has advanced significantly to allow the development of Brian Computer Music Interface (BCMI) devices. BCMI devices are simply the application of BCI technology to generate music as an output and/or feedback to the user. The creation of consciously controlled real-time EEG generation of scalar music by a user with experimentally proven accuracy has not yet been described, to the best of our knowledge.
In this paper, we describe the creation of the Encephalophone, a BCMI device that uses visual cortex posterior dominant rhythm (PDR) or motor cortex mu rhythm (mu) to consciously and volitionally control the generation of scalar music. This represents a novel musical instrument that does not require physical movement, as well as a potential therapeutic device for patients suffering from various motor deficits (e.g. amyotrophic lateral sclerosis, brainstem stroke, traumatic amputation). We additionally describe experiments demonstrating the user-controlled accuracy of the instrument by conscious cognitive processes.